1. Field of the Invention
The present invention relates generally to business process modeling, and in particular to a computer implemented method, data processing system, and computer program product for providing an open, generalized and reusable parametric optimization framework and architecture which enables rapid deployment of optimization solutions to any tool or program that builds on the Eclipse™ platform.
2. Description of the Related Art
Business process management (BPM) comprises tools which enable business analysts to model, simulate, and analyze complex business processes quickly and effectively. A business process is a process that provides some service for a requesting application, a user, or a customer. The business process may comprise a collection of interrelated tasks, which solve a particular issue.
Within the business process management space, business process simulation, or testing of the business processes in the field, has become an important feature. An objective of business process simulation is to implement what-if scenarios of the measured goals (e.g., processing time, cost, wait time, etc.) over a set of input parameters (e.g., duration of task A, cost of resource B, etc). A problem with existing business process simulation methods is that if a business user wants to determine what combination of input parameters can give the best or optimal results of the measured goals, the user may need to simulate many times with different combinations of the input parameters. Consequently, a lot of effort may be needed to set up the parameters, start the simulations, record the simulation results, compare what combinations have given the best results, or even plot graphs to see the trend of the measured goals with all of the different parameter combinations. Even more problematic is that the simulation procedure is performed mostly manually.
While some optimization solutions for business processes are currently available, these existing optimization solutions employ proprietary optimization technology and solutions. Consequently, no reusability or extensibility of the solutions is possible. In addition, the existing optimization solutions may not plug into other different optimization software, nor interact with other user data models other than the proprietary data model with fixed input or output attributes for optimizing.